This letter presents a novel optimal control approach for systems represented by a multi-model, i.e., a finite set of models, each one corresponding to a different operating point. The proposed control scheme is based on the combined use of model predictive control (MPC) and first order integral sliding mode control. The sliding mode control component plays the important role of rejecting matched uncertainty terms possibly affecting the plant, thus making the controlled equivalent system behave as the nominal multi-model. A min-max multi-model MPC problem is solved using the equivalent system without further robustness oriented add-ons. In addition, the MPC design is performed so as to keep the computational complexity limited, thus facilitating the practical applicability of the proposal. Simulation results show the effectiveness of the proposed control approach.

The power converters are widely used in several industrial applications where it is necessary to obtain from a fixed voltage another one higher or lower than the original. In this paper, we focus on the DC-DC (direct current) boost converters, where to guarantee the desired voltage, an internal current tracking loop is usually used. However, this tracking cannot be assured in the presence of unknown load changes and external perturbations when traditional controller strategies are implemented. In this paper, an advanced control strategy is proposed to ensure the current tracking using a saturated super-twisting controller on the power converter. The finite-time current tracking of a DC-DC boost converter is assured in the presence of bounded Lipschitz perturbations composed by unknown load changes and exogenous signals. The proposed approach generates a continuous bounded control signal applied to the converter by using a sigma-delta modulator . The controller gains are tuned to obtain finite-time stabilization of the tracking error, while the control signal remains bounded. To illustrate the effectiveness of the proposed results, the controller is applied to a physical boost converter using the hardware implemented and an STM32 Discovery development card. Besides, the controller is compared with a first-order sliding mode controller showing that for small sample times, the energy of the error signal is reduced.

Robust generation of self‐oscillation for mechanical systems in the presence of matched uncertainties/perturbations based on a two‐relay controller, which naturally has a switched structure, is presented. Two scenarios are considered: the case when all the system states are available and the case when only output information is measured. In both scenarios, a continuous switched integral sliding modes controller is implemented to guarantee theoretically exact compensation of matched uncertainties/perturbations before the first switching. The chattering attenuation in the presence of fast parasitic dynamics can be realised by a switched gain strategy of the super‐twisting algorithm. In the output‐based scenario, the observer and the controller are initiated sequentially. A super twisting‐based observer is used, guaranteeing theoretically exact reconstruction of the states before the controller is activated. The observer gains are also switched to diminish the chattering. Once the observer has converged, the reconstructed state is used in the controller to achieve the control objective. The observer and controller are designed to attain convergence before the first switching.

This contribution presents a continuous integral sliding mode methodology for disturbed linear time-invariant systems with input and state asymmetric constraints affected by matched disturbances. This methodology preserves the nominal behavior of a system generated by a saturated nominal control law in finite time. It generates a bounded continuous control signal that satisfies the asymmetric constraints and compensates for the effects of matched bounded Lipschitz asymmetric disturbances in finite time. The proposed approach ensures that the state fulfils the constraints from the initial time. It is shown that when the initial conditions are outside the state constraints, the system solution converges to the constrained set. Simulations and experimental validation illustrate the applicability of the proposed approach.